@article{Iskandaryan2021a,
title = {Features Exploration from Datasets Vision in Air Quality Prediction Domain},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.3390/atmos12030312},
issn = {2073-4433},
year = {2021},
date = {2021-02-28},
journal = {Atmosphere},
volume = {12},
number = {3},
pages = {312},
abstract = {Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.},
keywords = {air quality prediction, data, machine learning},
pubstate = {published},
tppubtype = {article}
}